Techniques for analytical instrument performance diagnostics

ABSTRACT

Techniques and apparatus for diagnostic processes for analytical instruments are described. In one embodiment, for example, an apparatus may include at least one memory, and logic coupled to the at least one memory. The logic may be configured to receive diagnostic information associated with at least one analytical instrument, and process the diagnostic information using a computational model to generate at least one diagnostic model for at least one diagnostic. Other embodiments are described.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No.62/859,565, filed Jun. 10, 2019 and entitled “TECHNIQUES FOR ANALYTICALINSTRUMENT PERFORMANCE DIAGNOSTICS”, which is hereby incorporated byreference.

TECHNICAL FIELD

Embodiments herein generally relate to managing analytical instruments,and, more particularly, to processes for monitoring analyticalinstrument performance and diagnosing analytical instrument abnormaloperating conditions.

BACKGROUND

The performance of analytical instruments is continually monitored toensure data quality. Analytical instruments, such as mass spectrometry(MS) and/or liquid chromatography-mass spectrometry (LC-MS) systems arecapable of providing detailed characterization of complex sample sets.However, the ability to perform analyses to obtain precise, detailedanalytical data causes MS and LC-MS systems to be susceptible tooperational instability, including hardware and/or software issues thatare difficult to detect and diagnose. Such issues may be due to anexisting or imminent component failure, human error, incorrect systemconfiguration, and/or the like. In conventional systems, determining aroot cause typically involves an extensive and time-consuming process oftrial and error performed by operators that may be experienced withrunning methods on an analytical instrument, but may not have adequateknowledge of particular system components to efficiently determine adiagnosis.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an embodiment of a first operating environmentaccording to an embodiment.

FIG. 2 illustrates an embodiment of a second operating environment.

FIG. 3 illustrates an embodiment of a third operating environment.

FIG. 4 depicts a configuration structure for a diagnostic.

FIGS. 5-16 illustrate graphs and associated data capture files forvarious diagnostic scenarios according to some embodiments.

FIGS. 17-39 depict illustrative diagnostic scenarios for achromatography system according to some embodiments

FIG. 40 illustrates an embodiment of a computing architecture.

DETAILED DESCRIPTION

Various embodiments may generally be directed toward systems, methods,and/or apparatus for monitoring the performance of analyticalinstruments. In some embodiments, a diagnostic services process may beoperative to determine standard operating conditions for an analyticalinstrument and/or components thereof. In various embodiments, standardoperating conditions may include analytical instrument operationassociated with standard reference data (for instance, datacorresponding to normal, expected, and/or the like operation of theanalytical instrument). In exemplary embodiments, the diagnosticservices process may operate to determine non-standard operatingconditions for the analytical instrument and/or components thereof. Invarious embodiments, non-standard operating conditions may includeanalytical instrument operation associated with non-standard referencedata (for instance, data corresponding to abnormal, unexpected,component failure (or imminent failure) condition, instrument errorcondition, and/or the like operation of the analytical instrument). Insome embodiments, the diagnostic services process may operate todetermine a cause of a non-standard operating condition.

In various embodiments, data generated during operation of an analyticalinstrument and/or components thereof may be accessed, for instance, viadata channels. Some embodiments may implement automated computersoftware with machine learning via a neural network and big data, forinstance, large data sets that may be analyzed computationally to revealpatterns, trends, and associations between data channels. The diagnosticservices process may operate to perform training processes using thedata. Non-limiting examples of training processes may include developingpass/fail input/output function(s) to determine the behavior of a giveninstrument against a pool of recorded repeated positive/negative teststhat occur in the normal operation of the instrument/system (forexample, initialization, flow, or running an injection for a liquidchromatography-mass spectrometry (LC-MS) system). The diagnosticservices processes may operate to perform testing processes, forinstance, detecting and/or diagnosing instrument issue(s) (for instance,non-standard operating conditions) for an analytical instrument and/orcomponents thereof. In various embodiments, the testing processes mayinclude applying the pass/fail input/output function(s) topre-determined tests (for instance, determined during the trainingprocesses) and providing the results and data collected to a store oftraining data.

In various embodiments, analytical instruments and/or components thereofmay generate operating data in various possible scenarios including,without limitation, at rest, a short diagnostic test, or while undernormal operation (for instance, performing an analysis method). In anexemplary embodiment, the operating data may be or may includetime-based digitized binary analog data from internal devices. In someembodiments, the channel data may be subscribed to and read in via alogic component, processor, controller, circuitry, and/or the likeresiding on a computing device operably coupled to the analyticalinstrument and/or components thereof. In some embodiments, a system mayinclude one or more analytical instruments, components thereof, and/orassociated devices (for instance, a sample device, a reagent supplydevice, and/or the like). Each system may include and/or be associatedwith one or more logic components (or controllers).

In some embodiments, a logic component may receive configurationinformation for an analytical instrument and/or components thereof. Invarious embodiments, configuration information may include, withoutlimitation, operating configurations, modes, ranges, parameters,thresholds, and/or the like. In some embodiments, a logic component mayreceive a configuration data item to perform a training process or modeusing configuration data, channel data and associated diagnosis storedin a data store and input into one or more computational models (forinstance, neural network(s)) to create a model per instrument, system,device, component, and/or the like, and/or operating mode,configuration, and/or the like thereof. In some embodiments, the logiccomponent may receive a configuration data item to perform a testingprocess or mode using configuration data, real time channel data inputinto one or more computational models (for instance, neural network(s))using model per instrument, system, device, component, and/or the like,and/or operating mode, configuration, and/or the like thereof determineone or more diagnosis. In various embodiments, test channel data and/ordiagnosis information may be added to a training datastore. In someembodiments, a diagnosis for an instrument, system, device, component,and/or the like, and/or operating mode, configuration, and/or the likethereof may be provided to an operator for review, analysis, annotation(or “mark-up”), and/or the like.

In some embodiments, the diagnostic services process may include amachine learning framework having a form of y=f(x), where y is theoutput, f is a prediction function, and x is the input. For example, fora training process, given a training set of labeled examples {(x1,y1), .. . , (xN,yN)}, prediction function f may be estimated to minimize theprediction error on the training set. In another example, for a testingprocess, f may be applied to new test example x and output predictedvalue y=f(x) may be determined.

In various embodiments, computational models may be generated foranalytical systems, which may include analytical instruments, analyticalinstrument components, and/or associated devices. The computationalmodels may include standard models and/or model data associated withstandard operations (for instance, normal, expected, proper,within-range or threshold, and/or the like) in which the systems and/orcomponents thereof (for instance, analytical instrument components,and/or associated devices) are operating properly, within acceptedlimits, and/or the like. The computational models may includenon-standard models and/or non-standard model data associated withnon-standard operations in which the systems and/or components thereof(for instance, analytical instrument components, and/or associateddevices) are operating improperly, outside of accepted limits, and/orthe like. In this manner, system failures and maintenance may bepredicted via predictive modeling of systems and/or system components.Accordingly, diagnostic service processes according to some embodimentsmay determine when a system and/or system component has failed and/orpredict when a system and/or system component will fail and address suchissues earlier and with more accuracy than conventional systems thatrely on operator-implemented trial-and-error techniques. Accordingly,systems operating according to some embodiments may achieve greaterlevels of operational efficiency as compared with conventional systems.

Some embodiments may operate diagnostic processes at various levels. Forexample, a level may include at the unit level, which may involvelow-level discrete electro-mechanical sensing such as a motor, asolenoid, a column, and/or the like. In another example, a level mayinclude a component level, which may involve sub-assembly component(s)such as a pump, heater cooler, sample handling, and/or the like. In afurther example, a level may include a qualification level, which mayinclude functional domain level such as product initialization,instrument qualification, instrument testing, and/or the like. In otherexamples, a level may include a comprehensive level, which may involvean application level representing the system, which may be inclusive toany post processed data (for instance, monitor instrument data(raw/processed) to correlate patterns and trends with specific hardwareor method issues).

In this description, numerous specific details, such as component andsystem configurations, may be set forth in order to provide a morethorough understanding of the described embodiments. It will beappreciated, however, by one skilled in the art, that the describedembodiments may be practiced without such specific details.Additionally, some well-known structures, elements, and other featureshave not been shown in detail, to avoid unnecessarily obscuring thedescribed embodiments.

In the following description, references to “one embodiment,” “anembodiment,” “example embodiment,” “various embodiments,” etc., indicatethat the embodiment(s) of the technology so described may includeparticular features, structures, or characteristics, but more than oneembodiment may and not every embodiment necessarily does include theparticular features, structures, or characteristics. Further, someembodiments may have some, all, or none of the features described forother embodiments.

As used in this description and the claims and unless otherwisespecified, the use of the ordinal adjectives “first,” “second,” “third,”etc. to describe an element merely indicate that a particular instanceof an element or different instances of like elements are being referredto, and is not intended to imply that the elements so described must bein a particular sequence, either temporally, spatially, in ranking, orin any other manner.

FIG. 1 illustrates an example of an operating environment 100 that maybe representative of some embodiments. As shown in FIG. 1 , operatingenvironment 100 may include a system 105 operative to manage informationassociated with devices 115 a-n. In some embodiments, devices 115 a-nmay be or may include an analytical instrument, components thereof (forinstance, pumps, valves, instrument modules, and/or the like), devicesassociated therewith (for instance, sample management systems, reagentsystems, and/or the like), and/or the like. In some embodiments,analytical instruments may be or may include a chromatography system, aliquid chromatography (LC) system, a gas chromatography (GC) system, amass analyzer system, a mass detector system, a mass spectrometer (MS)system, an ion mobility spectrometer (IMS) system, a high-performanceliquid chromatography (HPLC) system, a ultra-performance liquidchromatography (UPLC®) system, a ultra-high performance liquidchromatography (UHPLC) system, an ultraviolet (UV) detector, a visiblelight detector, a solid-phase extraction system, a sample preparationsystem, a sample introduction system, a pump system, a capillaryelectrophoresis instrument, combinations thereof, components thereof,variations thereof, and/or the like. Although LC, MS, and LC-MS are usedin examples in this detailed description, embodiments are not solimited, as other analytical instruments capable of operating accordingto some embodiments are contemplated herein.

In various embodiments, devices 115 a-n may be or may include variouscomponents, modules, and/or the like. Non-limiting examples ofcomponents may include detectors, solvent managers, sample managers,pumps, valves, electrodes, quadrupole elements, columns, ion sourcedevices, sensors (for instance, pressure sensors, temperature sensors,flow sensors, and/or the like), data system hardware, data systemsoftware, and/or the like. In various embodiments, a device componentmay include any component that may be individually monitored (forinstance, associated with operating information). In some embodiments,device 115 a-n may be or may include an instrument system. In general,an instrument system may include a collection of instrument modules thatoperate collectively as a holistic unit. For example, an instrumentsystem may include an analytical device (for instance, an LC or MSdevice) capable of providing analytical data (measurements) andsupporting components (for example, an injector, detector, pump, controlsystems, and/or the like for an LC or MS system) that facilitate thegeneration of the analytical data by the analytical device. Embodimentsare not limited in this context.

In some embodiments, devices 115 a-n may operate to perform an analysisand generate analytical information 144. In various embodiments,analytical information 144 may include information, data, files, charts,graphs, images, textual information, and/or the like generated by ananalytical instrument as a result of performing an analysis method. Forexample, for an LC-MS system, a device 115 a-n may separate a sample andperform mass analysis on the separated sample according to a specifiedmethod to generate analytical information 144 that may include raw,native, or otherwise unprocessed data, chromatograms, spectra, peaklists, mass values, retention time values, concentration values,compound identification information, and/or the like. In variousembodiments, analytical information 144 may include informationresulting from a quality control process, such as a system suitabilitytest, quality control check, and/or the like. In some embodiments,system 105 may include a plurality of devices 115 a-n. In variousembodiments, system 105 may include a single device 115 a-n.

In various embodiments, system 105 may include computing device 110communicatively coupled to devices 115 a-n or otherwise configured toreceive and store diagnostic information associated with devices 115 a-n(for instance, stored in data store 154 a-n or other remote data storagestructure, including a cloud storage environment). In general,diagnostic information may include information associated with a device115 a-n that may be used to generate a diagnostic associated with thedevice 115 a-n. In some embodiments, diagnostic information may includeanalytical information 144 and/or operating information 146. Analytical(or analysis) information 144 may include information generated bydevices 115 a-n as a result of performing an analysis. For example, foran LC-MS device, analytical information 144 may include, withoutlimitation, compound lists, peaks, peak lists, quality control (QC)results, spectra, graphs, charts, and/or the like. In variousembodiments, operating information 146 may include informationassociated with the operation of devices 115 a-n. For example, operatinginformation 146 may include a voltage of a pump used by an LC-MS deviceduring the performance of an analysis. Non-limiting examples ofoperating information 146 may include voltages, currents, power, flowinformation (for instance, flow rate, blockage detection, and/or thelike), component positions (for instance, valve open/close position),alarms, alarm codes, vibration detection, audio detection, video images,pressures, temperatures, software status, hardware status, and/or thelike. In some embodiments, operating information 146 may be obtained viaone or more data channels 117 a-n associated with devices 115 a-n,components thereof, sensors thereof (for instance, a pressure transducerassociated with a pump, a position sensor associated with a valve, avideo recorder associated with a component, and/or the like), and/or thelike. Embodiments are not limited in this context.

In various embodiments, devices 115 a-n and/or data channels 117 a-n mayoperate to provide analytical information 144 and/or operatinginformation 146 directly to computing device 110 and/or to a location ona network 150 accessible to computing device 110, such as a cloudcomputing environment, nodes 152 a-n, data stores 154 a-n, and/or thelike. In various embodiments data channels 117 a-n may operate usingvarious forms of data including, without limitation, numeric,alphanumeric, graphical, textual, visual, audio, images, binary, and/orthe like. Embodiments are not limited in this context.

In some embodiments, computing device 110 may be operative to control,monitor, manage, or otherwise process various operational functions ofdevice 115 a-n. In some embodiments, computing device 110 may beoperative to provide analytical information 144 and/or operatinginformation to a location on network 150 through a secure orauthenticated connection. In some embodiments, computing device 110 maybe or may include a stand-alone computing device, such as a personalcomputer (PC), server, tablet computing device, cloud computing device,mobile computing device (for instance, a smart phone, tablet computingdevice, and/or the like), data appliance, and/or the like. In variousembodiments, computing device 110 may be or may include a controller orcontrol system integrated into device 115 a-n to control operationalaspects thereof.

Although only one computing device 110 is depicted in FIG. 1 ,embodiments are not so limited. In various embodiments, the functions,operations, configurations, data storage functions, applications, logic,and/or the like described with respect to computing device 110 may beperformed by and/or stored in one or more other computing devices. Asingle computing device 110 is depicted for illustrative purposes onlyto simplify the figure.

Computing device 110 may include processor circuitry 120, a memory unit130, and a transceiver 160. Processor circuitry 120 may becommunicatively coupled to memory unit 130 and/or transceiver 160.

Processor circuitry 120 may include and/or may access various logic forperforming processes according to some embodiments. For instance,processor circuitry 120 may include and/or may access device model logic124 and/or diagnostic logic 126. Processing circuitry 120 and/or devicemodel logic 124 and/or diagnostic logic 126, and/or portions thereof,may be implemented in hardware, software, or a combination thereof. Asused in this application, the terms “logic,” “component,” “layer,”“system,” “circuitry,” “decoder,” “encoder,” and/or “module” areintended to refer to a computer-related entity, either hardware, acombination of hardware and software, software, or software inexecution, examples of which are provided by the exemplary computingarchitecture 4000. For example, a logic, circuitry, controller,processor, and/or the like may be and/or may include, but are notlimited to, a process running on a processor, a processor, a hard diskdrive, multiple storage drives (of optical and/or magnetic storagemedium), an object, an executable, a thread of execution, a program, acomputer, hardware circuitry, integrated circuits, application specificintegrated circuits (ASIC), programmable logic devices (PLD), digitalsignal processors (DSP), field programmable gate array (FPGA), asystem-on-a-chip (SoC), memory units, logic gates, registers,semiconductor device, chips, microchips, chip sets, software components,programs, applications, firmware, software modules, computer code,combinations of any of the foregoing, and/or the like.

Although diagnostic services logic 122 is depicted in FIG. 1 as beingwithin processor circuitry 120, embodiments are not so limited. Inaddition, although device model logic 124 and diagnostic logic 126 aredepicted as being a logic of diagnostic services logic 122, embodimentsare not so limited, as device model logic 124 and diagnostic logic 126may be separate logics and/or may be standalone logics. For example,diagnostic services logic 122, and/or any component thereof, may belocated within an accelerator, a processor core, an interface, anindividual processor die, implemented entirely as a software application(for instance, analytical services application 150) and/or the like.

Memory unit 130 may include various types of computer-readable storagemedia and/or systems in the form of one or more higher speed memoryunits, such as read-only memory (ROM), random-access memory (RAM),dynamic RAM (DRAM), Double-Data-Rate DRAM (DDRAM), synchronous DRAM(SDRAM), static RAM (SRAM), programmable ROM (PROM), erasableprogrammable ROM (EPROM), electrically erasable programmable ROM(EEPROM), flash memory, polymer memory such as ferroelectric polymermemory, ovonic memory, phase change or ferroelectric memory,silicon-oxide-nitride-oxide-silicon (SONOS) memory, magnetic or opticalcards, an array of devices such as Redundant Array of Independent Disks(RAID) drives, solid state memory devices (e.g., USB memory, solid statedrives (SSD) and any other type of storage media suitable for storinginformation. In addition, memory unit 130 may include various types ofcomputer-readable storage media in the form of one or more lower speedmemory units, including an internal (or external) hard disk drive (HDD),a magnetic floppy disk drive (FDD), and an optical disk drive to readfrom or write to a removable optical disk (e.g., a CD-ROM or DVD), asolid state drive (SSD), and/or the like.

Memory unit 130 may store an analytical services application 150 thatmay operate, alone or in combination with diagnostic services logic 122,to perform various diagnostic process functions according to someembodiments. In various embodiments, analytical services application 150may interact with devices 115 a-n and/or components thereof throughvarious drivers (for instance, application programming interfaces (APIs)and/or the like), software and/or hardware interfaces, and/or the like.

In various embodiments, diagnostic services logic 122 may be configuredto provide and/or implement diagnostic services for devices 115 a-n. Insome embodiments, diagnostic services may include processes fordetermining whether devices 115 a-n and/or components thereof areoperating normally or abnormally. In various embodiments, diagnosticservices may include processes for predictive maintenance, for instance,determining whether devices 115 a-n and/or components thereof mayoperate abnormally in the future (for instance, detecting an imminentfailure condition). In exemplary embodiments, diagnostic services mayinclude processes for diagnosing a source of abnormal operation (forinstance, a failing pump, an inadequate seal, degraded reagent, and/orthe like). Embodiments are not limited in this context.

Device model logic 124 may operate to generate models associated withdevices 115 a-n and/or components thereof. In some embodiments, themodels may be stored as diagnostic models 142. In various embodiments,the models may include various computational models, including, withoutlimitation, neural network models. Non-limiting examples of acomputational model may include a machine-learning model, an artificialintelligence (AI) model, a neural network (NN), an artificial neuralnetwork (ANN), a convolutional neural networks (CNN), a deep learning(DL) network, a deep neural network (DNN), a recurrent neural network(RNNs), combinations thereof, variations thereof, and/or the like.Embodiments are not limited in this context. In some embodiments, eachdiagnostic model 142 may be associated with at least one diagnostic.

In some embodiments, diagnostic models 142 may be or may include aneural network. For example, FIG. 2 illustrates an example of anoperating environment 200 that may be representative of variousembodiments implementing a neural network 225. Although neural networkmay be used as examples herein, embodiments are not so limited, as anytype of model capable of operating according to some embodiments iscontemplated herein. In general, a neural network 225 may be formed of aplurality of layers, including input layer 250, hidden layers 260 a-n,and output layer 270. Each layer of neural network 225 may include oneor more neurons 280 a-n. In general, a neuron 280 a-n may be acomputational unit that takes in one or more inputs, applies a functionto the inputs (for instance, an activation function), and generates anoutput. The neurons 280 a-n may be interconnected such that the outputfrom neurons 280 a-n in a first layer may be input to the neurons 280a-n in a subsequent layer (and vice versa). For example, the output ofneuron 280 a of input layer 250 may be provided as input to neurons 280d-f of hidden layer 260 a. In general, input layer 250 and output layer270 may be visible (for instance, accessible for network input or asnetwork output, respectively) outside of neural network 225, such as tosoftware, agents, and/or the like. Hidden layers 260 a-n are notaccessible externally from neural network 225 and operate as a hidden(or deep) processing structure for processing (for instance, trainingand/or analyzing) input data received from input layer 250.

Results from the training or analysis may be provided to output layer280 and transmitted as output 230, for example, to a data consumer. Forexample, in some embodiments, the diagnostic services process mayinclude a neural network having a form of y=f(x), where y is the output,f is a prediction function, and x is the input. For example, for atraining process, given a training set of labeled examples {(x1,y1), . .. , (xN,yN)}, neural network 225 may implement prediction function f byminimizing the prediction error on the training set. In another example,for a testing process, neural network may apply f to new test example xand output the predicted value y=f(x). Embodiments are not limited inthis context.

In some embodiments, device model logic 124 may generate and trainmodels based on training information 140. In various embodiments,training information 140 may be generated by an operator (for instance,artificial operating data), generated by operating devices 115 a-nand/or components thereof, or combinations thereof. For example, anoperator may provide training information 140 generated during theactual operation of a device component, such as a pump, to computingdevice 110 as training information 140 (for instance, authenticoperating data). In another example, devices 115 a-n and/or componentsthereof may be operated to generate training information 140. In someembodiments, operating information 140 may be generated for normaland/or abnormal operation of devices 115 a-n and/or components thereof.In one example, an operator may provide operating information 140 thatincludes channel data associated with operation of a pump under normalconditions. In another example, the operator may provide operatinginformation that includes channel data associated with abnormaloperation of the pump, such as a low voltage condition. The operatinginformation 140 may be designated as being normal/abnormal depending onthe operating conditions.

Device model logic 124 may apply a computational model, learningprocess, and/or the like to training information 140 to train thediagnostic model. In various embodiments, a diagnostic model may begenerated for one or more diagnostics. In general, a diagnostic ordiagnostic unit may include an entire device or system (for instance,group of instruments) or at least one monitored unit of devices 115 a-n,channels 117 a-n, components thereof, functions thereof, propertiesthereof, and/or the like. For example, a diagnostic may include a device115 a-n, such as an LC-MS device. In another example, a diagnostic mayinclude a component of a device 115 a-n, such as a column, a pump, avalve, and/or the like. In a further example, a diagnostic may include afunction of a device 115 a-n and/or a component thereof, such ascomponent operation (for instance, opening/closing of a valve, pumpoperation), and/or the like. In an additional example, a diagnostic mayinclude a property of a device 115 a-n and/or a component thereof, suchas a temperature, pressure, voltage, status (for instance, on/off,open/closed, and/or the like), and/or the like. In various embodiments,diagnostic model logic 124 may continually train diagnostic models 142with new information to improve the diagnostic and predictive abilitiesof diagnostic models 142 (for instance, to achieve a “big data” datastore, leading to diagnostic models 142 becoming more accurate).

In some embodiments, diagnostic logic 126 may test a diagnostic model142, for example, against actual data (for instance, actual channeldata) to determine a diagnosis 148, which may include a prediction,operating condition, and/or the like. For example, diagnostic logic mayimplement y=f(x), where y is the output (for instance, a diagnosis 148),f is a prediction function (for instance, a diagnostic model 142), and xis the input (operating information 146 in the form of actual channeldata). For example, data channel 117 a may include voltage operatinginformation 146 for a pump of device 115 a. Diagnostic logic 126 may usea diagnostic model 142 associated with the pump and/or data channel 117a to receive actual data via data channel 117 a as input and maygenerate a diagnosis relating to whether the pump is operating properlyand/or make a diagnosis 148 that includes prediction about the operatinglife of the pump (for instance, an X % chance of failure within acertain time period).

For example, for a System 1 that includes Device 1-3, the diagnosis 148may include the following: System 1 100% OK, Device 1 100%, Device 2100%, Device 3 100%. The diagnosis 148 for System 1 may indicate thatSystem 1 is operating OK at 100% and that Devices 1-3 are operating at100%. In another example, a diagnosis 148 for System 2 having Device 4-6may include the following: System 2 66% OK, Device 4 0%, Device 5 100%,Device 6 100%. The diagnosis 148 for System 2 may indicate that System 2is 66% OK and that Device 4 is operating at 0%. The diagnosis 148 mayfurther predict or otherwise indicate that Device 4 may be the cause ofthe 66% operational condition of System 2. In various embodiments, theoperating information 146 used to generate the diagnoses 148 for System1 and/or System 2 may be added to training information 140 to increasethe volume of data that may be used to generate diagnostic models 142(for instance, to achieve “big data” to increase model accuracy).

Machine learning according to some embodiments in an analyticalenvironment may provide multiple technological advantages overconventional systems. One non-limiting technological advantage mayinclude improving the speed and accuracy of instrument/system diagnosismany times over what even an expert user can achieve. Anothernon-limiting technological advantage may include, after training on theinitial training data sets, diagnostic models 142 may be continually andautomatically updated on startup/set intervals and improved based onadditional captures (for instance, made at a customer site) by way of“big data” that may be analyzed computationally to reveal patterns,trends, and associations that may not be efficiently and/or accuratelydetermined via conventional operator methods.

FIG. 3 illustrates an embodiment of a third operating environment. Asshown in FIG. 3 , operating environment 300 may include a plurality ofinstrument systems 315 a-n communicatively coupled to a systemcontroller 320 a via system diagnostic channels 317. In someembodiments, multiple diagnostic channels 317 may be available forinspection to diagnose internal devices. In various embodiments, theremay be one diagnostic channel 317 per monitored element, such as one persensor. For example, for an LC-MS system, one or more diagnosticchannels 317 may be generated via test/injection, and/or the like.

In some embodiments, system controller 320 may include processingconfiguration 322 and processor 324. In various embodiments, there maybe one system controller 320 per instrument system 315 a-n. In variousother embodiments, a system controller 320 may interact with a pluralityof instrument systems 315 a-n. Processor 320 may implement variousmodelling processes (for instance, via machine learning processes)according to some embodiments. For example, processor 320 may implementa training mode and/or a testing mode. In some embodiments, trainingmode may include using configuration data, specified channel data andassociated diagnoses stored in a data store (for instance, a local datastore, a cloud data store, and/or the like) as input into a neuralnetwork diagnostic model to create a diagnostic model per diagnosticunit (for instance, instrument, system (for example, group ofinstruments and/or instrument components), device, component, sensor,element, and/or the like). In some embodiments, training mode mayinclude developing pass/fail input/output function(s) via automatedmachine learning processes to determine the behavior of a giveninstrument and/or component thereof against a pool of many recordedrepeated positive/negative tests (for instance, discrete, simple tests)that occur in the normal operation of the instrument/system (forexample, initialization, flow, injection, and/or the like).

In various embodiments, testing mode may include using configurationdata and actual (for instance, real-time or substantially real-time)channel data input into an existing neural network diagnostic model fora diagnostic unit to predict a diagnosis 326 a-n for the diagnosticunit. In exemplary embodiments, testing mode may include diagnosingissue(s) for a given instrument and/or component thereof after applyingthe previously obtained pass/fail input/output function(s) to thepositive/negative tests of the training mode and adding the results anddata collected to the training pool.

In some embodiments, diagnoses 326 a-n may be associated with adiagnostic unit and a diagnostic model. For example, diagnosis 326 a maybe associated with a first diagnostic (for instance, an entireinstrument system) and a particular neural network (Neural Network 1)generated for the first diagnostic. In another example, diagnosis 326 bmay be associated with a second diagnostic (for example, a particulardevice of an instrument system, such as a pump) and a second neuralnetwork (Neural Network 2) associated with the second diagnostic. Fordiagnoses 326 a, processor 324 may receive channel data associated withthe first diagnostic and provide the channel data to the correspondingmodel (for instance, Neural Network 1) to generate diagnosis 326 a.

In exemplary embodiments, a processed diagnosis for an instrument system315 a-n (for instance, an entire instrument system and each device ofthe instrument system) may be reported to the instrument system (forinstance, in the form Instrument System A 315 a 100% OK, Device 1 100%,Device 2 100%, Device 3 100%; and Instrument System B 66% OK, Device 40%, Device 5 100%, Device 6 100%; and/or the like). In variousembodiments, diagnoses 326 a-n may be stored in data store 354 and/orprovided to various nodes 330 a-n, such as an operator node, a trainingsupervisor node, a data system node, and/or the like. In someembodiments, a node 330 a-n may be operated by a training supervisor orother subject matter expert who may review, annotate, or otherwiseanalyze diagnoses 326 a-n, channel data, and/or the like. In variousembodiments, the training supervisor may mark-up channel data,diagnoses, and/or the like to improve training data, diagnostic models,and/or the like.

In some embodiments, one or more diagnostic units (for instance,instrument systems and/or components thereof) may generate time-baseddigitized binary analog data from internal devices in various possiblescenarios, including, without limitation, at rest, a diagnostic test,normal operation (for instance, running a method), and/or the like.

Channel data (for instance, from channels 317) may be subscribed to andread in via a process “Processor” (for instance, processor 324) thatresides on a computing device (for instance, system controller 320) thatis used to coordinate and control the multiple instruments. In someembodiments, there may be one “System Controller” per system (forinstance, group of instruments).

The Processor may read one or more configuration data items to performthe following:

-   -   Training mode—using configuration data, specified channel data,        and/or associated diagnosis stored in a data store (local/cloud        based) is input into one or more neural network(s) to create a        model per instrument/system/device diagnosis. A Training        supervisor (for instance, accessing information via Node A 330        a) may be a subject matter expert that is used to help markup        the initial training data. The machine learning models can be        created at any time, such as at set intervals, in the        background, on down time, or on the cloud; or    -   Testing mode—using configuration data, specified real time        channel data input into one or more neural network(s) using an        already created model per instrument/system/device to predict        one or more diagnosis. The test channel data along with        diagnosis may be added to a training datastore (for example,        data store 354). Testing for a model y=f(x) may include applying        f to new text example x to output the predicted value y.

The processed diagnosis for the entire instrument/system and each deviceon that instrument/system may be reported to a data consumer, such as anoperator, a connected data system, a training supervisor, theinstrument/system itself, and/or the like. As the Training datastoregets bigger (“big data”) the neural network models get more accurate.The training datastore can be a local or remote (i.e. Amazon WebService, or similar) database.

FIG. 4 depicts a configuration structure for a diagnostic. For example,configuration 405 may be a configuration file for an analyticalinstrument, such as an LC-MS instrument. In various embodiments,configuration 405 may delineate properties of one or more diagnosticmodels, such as inputs, outputs, channels, and/or the like (forinstance, in a *.json file). In various embodiments, a diagnostic may beassociated with a plurality of configurations, including, withoutlimitation, a capture configuration, a training configuration, atraining configuration, and/or the like.

In some embodiments, a first step for diagnostic processes according tosome embodiments may be to create the various models (for instance, onefor each component or diagnostic of the instrument). Configuration 405may be used to, among other things, list the inputs/outputs for creatingthe model for each diagnostic. In various embodiments, inputs mayinclude sensor data, instrument flow/age/alarmCode history, and/or thelike. After creating configuration 405, many recordings may be capturedof a good example of diagnostic operation. For example, for a pump,flowing at 1 mL/min for 1 minute using a software routine that can berun (on powerup/system startup/injection/ad-hoc). Diagnostic servicesapplication and/or a training supervisor may mark all diagnosis columnsas GOOD (1) (see, for example, FIGS. 7 and 14 ). A mass flow meter mayoptionally be used to verify flow rate is ideal ensuring the diagnosticservices application and/or training supervisor marks an appropriate‘good’ example. Many recordings of bad scenarios may then be captured ofeach component with the same criteria (for instance, flowing at 1 mL/minfor 1 minute). The neural network may then train against the captureddata to create the models. Finally, when a new capture is done at thecustomer site (on powerup/system startup/injection/ad-hoc), the data maybe fed into the diagnostic model (for instance, neural network) whichruns against each model giving a diagnosis indicator (for instance,percent OK) value for each component in the instrument/system.

Examples: Diagnostic Scenarios for a Chromatography System

FIG. 5 illustrates graph 505 of a real-time plot for a chromatographysystem for various diagnostics 510 according to some embodiments. FIGS.6 and 7 illustrates data capture files 605 and 705, respectively,relating to the data plotted in graph 505. In various embodiments,diagnosis columns may have a value of 1 if OK and 0 if BAD. The initialtraining data sets may have the diagnosis columns data modified by thediagnostic services application and/or subject matter experts andvarious ‘prime’ examples of BAD diagnosis are captured, to aid inobtaining optimal training data to generate the models. In variousembodiments, this may only be done initially, as under normal operation,the diagnosis data assignment may be automated. FIG. 8 depictsdiagnostic output 805 associated with predictions associated with theinformation plotted in graph 505. FIG. 9 illustrates graph 905 for a“bad transducers” scenario (for example, transducer output is 0)according to some embodiments, and FIG. 10 illustrates data capture file1005 relating to the data plotted in graph 905. FIG. 11 illustratesgraph 1105 for a “bad encoder” scenario according to some embodiments,and FIG. 12 illustrates data capture file 1205 (for instance,post-supervise) relating to the data plotted in graph 1105.

FIG. 13 illustrates graph 1305 of a “loss of prime” scenario accordingto some embodiments. For example, diagnosis of prime may involvedetermining when solvent has run out on the solvent supply end. In thescenario of FIG. 13 , for instance, accumulator pressure may not beholding, leading to up-and-down behavior. As depicted in data capturefile 1405 of FIG. 14 , the diagnostic services application and/ortraining supervisor may annotate this channel's diagnosis column“DiagnosticsDiagnosisPrime” as BAD (0).

FIG. 15 illustrates graph 1505 of a “bad check valve” (for instance, foran LC pump) scenario according to some embodiments. In the scenario ofFIG. 13 , for instance, accumulator pressure may have a “ripple” of >1%.For example, diagnosis of a “bad check valve” may occur when the checkvalve ball is not seating correctly allowing for siphoning of fluid asthe pump is performing pump strokes. The state of a check valve may berelated to its age and historical flow. As depicted in data capture file1605 of FIG. 16 , the diagnostic services application and/or trainingsupervisor may tag this channel's diagnosis column“DiagnosticsDiagnosisCheckValve” as BAD (0).

In some embodiments, another scenario for a pump component diagnosisversus flowing for 1 min is to capture training and test data during aleak test. The same or similar channels as in FIGS. 13 and 15 may becollected, however different diagnostic models may be used employing adistinct configuration file. The pump leak test performs a pressureramp-up and then employs feedback control to maintain constant pressurewithin the pump head. By monitoring plunger travel during the constantpressure phase, it calculates the leak rates of the primary andaccumulator actuators. These leak rates can indicate whether the checkvalves, tubes, fittings, plunger, or plunger seals are faulty. Thediagnostic services application and/or training supervisor may tag thischannel's diagnosis column “DiagnosticsDiagnosisFlowAccuracy” as BAD(0).

In various embodiments, diagnosis of a “bad flow accuracy” condition mayinvolve something preventing the full flow throughput of solventdelivered by the pump. Capture of a “bad flow accuracy” example ofone-minute flow at 1 mL/min in which, for instance, accumulator pressureand/or primary pressure is low and/or encoder acceleration is low. Inexemplary embodiments, diagnosis of a “bad seal” may occur when the pumppiston chamber has worn out seals, allowing for siphoning of fluid asthe pump is performing pump strokes. The state of the pump seals may berelated to its age and historical flow. The diagnostic servicesapplication and/or training supervisor may tag this channel's diagnosiscolumn “DiagnosticsDiagnosisSeal” as BAD (0). Diagnosis of a “badcolumn” may occur when the column degrades to the point of overlyconstricting or allowing flow. This is usually an effect of age of thecolumn and historical flow thru the column. Such a diagnosis may includea capture of a “bad column” example of 1 minute flow at 1 mL/min. In oneexample, accumulator pressure may have noticeable pressure ripples orhigher than normal pressures. The diagnostic services application and/ortraining supervisor may tag this channel's diagnosis column“DiagnosticsDiagnosisColumn” as BAD (0)

Various detector instruments may be used according to some embodiments,such as ultraviolet detector (UV), an LC detector that measures AU(Absorbance Units), and or detectors that may be used tocapture/train/test configuration files for TUV (Tunable UV), FLR, PDAdetectors, and/or the like. For example, in some embodiments, eachdiagnostic (for instance, an instrument) may be associated with aconfiguration file of its diagnostic settings (for instance,data_analyzer_config_diagnostic_capture_file_tuv.json,data_analyzer_config_diagnostic_train_file_tuv.json,data_analyzer_config_diagnostic_test_file_tuv.json, and/or the like).

FIGS. 17-39 depict illustrative diagnostic scenarios for achromatography system according to some embodiments. FIG. 18 depictsfailure indicators and failure causes for diagnostics for thechromatography system. Failure indicators are changes from benchmarkvalues is the failure indicator categories of changes in peak retentiontime, changes in peak area, changes in peak shape, changes in peakwidth, changes in system pressure and change in number of peaks.

FIG. 40 illustrates an embodiment of an exemplary computing architecture4000 suitable for implementing various embodiments as previouslydescribed. In various embodiments, the computing architecture 4000 maycomprise or be implemented as part of an electronic device. In someembodiments, the computing architecture 4000 may be representative, forexample, of computing device 110. The embodiments are not limited inthis context.

As used in this application, the terms “system” and “component” and“module” are intended to refer to a computer-related entity, eitherhardware, a combination of hardware and software, software, or softwarein execution, examples of which are provided by the exemplary computingarchitecture 4000. For example, a component can be, but is not limitedto being, a process running on a processor, a processor, a hard diskdrive, multiple storage drives (of optical and/or magnetic storagemedium), an object, an executable, a thread of execution, a program,and/or a computer. By way of illustration, both an application runningon a server and the server can be a component. One or more componentscan reside within a process and/or thread of execution, and a componentcan be localized on one computer and/or distributed between two or morecomputers. Further, components may be communicatively coupled to eachother by various types of communications media to coordinate operations.The coordination may involve the uni-directional or bi-directionalexchange of information. For instance, the components may communicateinformation in the form of signals communicated over the communicationsmedia. The information can be implemented as signals allocated tovarious signal lines. In such allocations, each message is a signal.Further embodiments, however, may alternatively employ data messages.Such data messages may be sent across various connections. Exemplaryconnections include parallel interfaces, serial interfaces, and businterfaces.

The computing architecture 4000 includes various common computingelements, such as one or more processors, multi-core processors,co-processors, memory units, chipsets, controllers, peripherals,interfaces, oscillators, timing devices, video cards, audio cards,multimedia input/output (I/O) components, power supplies, and so forth.The embodiments, however, are not limited to implementation by thecomputing architecture 4000.

As shown in FIG. 40 , the computing architecture 4000 comprises aprocessing unit 4004, a system memory 4006 and a system bus 4008. Theprocessing unit 4004 can be any of various commercially availableprocessors, including without limitation an AMD® Athlon®, Duron® andOpteron® processors; ARM® application, embedded and secure processors;IBM® and Motorola® DragonBall® and PowerPC® processors; IBM and Sony®Cell processors; Intel® Celeron®, Core (2) Duo®, Itanium®, Pentium®,Xeon®, and XScale® processors; and similar processors. Dualmicroprocessors, multi-core processors, and other multi-processorarchitectures may also be employed as the processing unit 4004.

The system bus 4008 provides an interface for system componentsincluding, but not limited to, the system memory 4006 to the processingunit 4004. The system bus 4008 can be any of several types of busstructure that may further interconnect to a memory bus (with or withouta memory controller), a peripheral bus, and a local bus using any of avariety of commercially available bus architectures. Interface adaptersmay connect to the system bus 4008 via a slot architecture. Example slotarchitectures may include without limitation Accelerated Graphics Port(AGP), Card Bus, (Extended) Industry Standard Architecture ((E)ISA),Micro Channel Architecture (MCA), NuBus, Peripheral ComponentInterconnect (Extended) (PCI(X)), PCI Express, Personal Computer MemoryCard International Association (PCMCIA), and the like.

The system memory 4006 may include various types of computer-readablestorage media in the form of one or more higher speed memory units, suchas read-only memory (ROM), random-access memory (RAM), dynamic RAM(DRAM), Double-Data-Rate DRAM (DDRAM), synchronous DRAM (SDRAM), staticRAM (SRAM), programmable ROM (PROM), erasable programmable ROM (EPROM),electrically erasable programmable ROM (EEPROM), flash memory, polymermemory such as ferroelectric polymer memory, ovonic memory, phase changeor ferroelectric memory, silicon-oxide-nitride-oxide-silicon (SONOS)memory, magnetic or optical cards, an array of devices such as RedundantArray of Independent Disks (RAID) drives, solid state memory devices(e.g., USB memory, solid state drives (SSD) and any other type ofstorage media suitable for storing information. In the illustratedembodiment shown in FIG. 40 , the system memory 4006 can includenon-volatile memory 4010 and/or volatile memory 4012. A basicinput/output system (BIOS) can be stored in the non-volatile memory4010.

The computer 4002 may include various types of computer-readable storagemedia in the form of one or more lower speed memory units, including aninternal (or external) hard disk drive (HDD) 4014, a magnetic floppydisk drive (FDD) 4016 to read from or write to a removable magnetic disk4018, and an optical disk drive 4020 to read from or write to aremovable optical disk 4022 (e.g., a CD-ROM or DVD). The HDD 4014, FDD4016 and optical disk drive 4020 can be connected to the system bus 4008by a HDD interface 4024, an FDD interface 4026 and an optical driveinterface 4028, respectively. The HDD interface 4024 for external driveimplementations can include at least one or both of Universal Serial Bus(USB) and IEEE 1384 interface technologies.

The drives and associated computer-readable media provide volatileand/or nonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For example, a number of program modules canbe stored in the drives and memory units 4010, 4012, including anoperating system 4030, one or more application programs 4032, otherprogram modules 4034, and program data 4036. In one embodiment, the oneor more application programs 4032, other program modules 4034, andprogram data 4036 can include, for example, the various applicationsand/or components of computing device 110

A user can enter commands and information into the computer 4002 throughone or more wire/wireless input devices, for example, a keyboard 4038and a pointing device, such as a mouse 4040. Other input devices mayinclude microphones, infra-red (IR) remote controls, radio-frequency(RF) remote controls, game pads, stylus pens, card readers, dongles,finger print readers, gloves, graphics tablets, joysticks, keyboards,retina readers, touch screens (e.g., capacitive, resistive, etc.),trackballs, trackpads, sensors, styluses, and the like. These and otherinput devices are often connected to the processing unit 4004 through aninput device interface 4042 that is coupled to the system bus 4008 butcan be connected by other interfaces such as a parallel port, IEEE 1394serial port, a game port, a USB port, an IR interface, and so forth.

A monitor 4044 or other type of display device is also connected to thesystem bus 4008 via an interface, such as a video adaptor 4046. Themonitor 4044 may be internal or external to the computer 4002. Inaddition to the monitor 4044, a computer typically includes otherperipheral output devices, such as speakers, printers, and so forth.

The computer 4002 may operate in a networked environment using logicalconnections via wire and/or wireless communications to one or moreremote computers, such as a remote computer 4048. The remote computer4048 can be a workstation, a server computer, a router, a personalcomputer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallyincludes many or all of the elements described relative to the computer4002, although, for purposes of brevity, only a memory/storage device4050 is illustrated. The logical connections depicted includewire/wireless connectivity to a local area network (LAN) 4052 and/orlarger networks, for example, a wide area network (WAN) 4054. Such LANand WAN networking environments are commonplace in offices andcompanies, and facilitate enterprise-wide computer networks, such asintranets, all of which may connect to a global communications network,for example, the Internet.

When used in a LAN networking environment, the computer 4002 isconnected to the LAN 4052 through a wire and/or wireless communicationnetwork interface or adaptor 4056. The adaptor 4056 can facilitate wireand/or wireless communications to the LAN 4052, which may also include awireless access point disposed thereon for communicating with thewireless functionality of the adaptor 4056.

When used in a WAN networking environment, the computer 4002 can includea modem 4058, or is connected to a communications server on the WAN 4054or has other means for establishing communications over the WAN 4054,such as by way of the Internet. The modem 4058, which can be internal orexternal and a wire and/or wireless device, connects to the system bus4008 via the input device interface 4042. In a networked environment,program modules depicted relative to the computer 4002, or portionsthereof, can be stored in the remote memory/storage device 4050. It willbe appreciated that the network connections shown are exemplary andother means of establishing a communications link between the computerscan be used.

The computer 4002 is operable to communicate with wire and wirelessdevices or entities using the IEEE 802 family of standards, such aswireless devices operatively disposed in wireless communication (e.g.,IEEE 802.16 over-the-air modulation techniques). This includes at leastWi-Fi (or Wireless Fidelity), WiMax, and Bluetooth™ wirelesstechnologies, among others. Thus, the communication can be a predefinedstructure as with a conventional network or simply an ad hoccommunication between at least two devices. Wi-Fi networks use radiotechnologies called IEEE 802.11x (a, b, g, n, etc.) to provide secure,reliable, fast wireless connectivity. A Wi-Fi network can be used toconnect computers to each other, to the Internet, and to wire networks(which use IEEE 802.3-related media and functions).

Numerous specific details have been set forth herein to provide athorough understanding of the embodiments. It will be understood bythose skilled in the art, however, that the embodiments may be practicedwithout these specific details. In other instances, well-knownoperations, components, and circuits have not been described in detailso as not to obscure the embodiments. It can be appreciated that thespecific structural and functional details disclosed herein may berepresentative and do not necessarily limit the scope of theembodiments.

Some embodiments may be described using the expression “coupled” and“connected” along with their derivatives. These terms are not intendedas synonyms for each other. For example, some embodiments may bedescribed using the terms “connected” and/or “coupled” to indicate thattwo or more elements are in direct physical or electrical contact witheach other. The term “coupled,” however, may also mean that two or moreelements are not in direct contact with each other, but yet stillco-operate or interact with each other.

Unless specifically stated otherwise, it may be appreciated that termssuch as “processing,” “computing,” “calculating,” “determining,” or thelike, refer to the action and/or processes of a computer or computingsystem, or similar electronic computing device, that manipulates and/ortransforms data represented as physical quantities (e.g., electronic)within the computing system's registers and/or memories into other datasimilarly represented as physical quantities within the computingsystem's memories, registers or other such information storage,transmission or display devices. The embodiments are not limited in thiscontext.

It should be noted that the methods described herein do not have to beexecuted in the order described, or in any particular order. Moreover,various activities described with respect to the methods identifiedherein can be executed in serial or parallel fashion.

Although specific embodiments have been illustrated and describedherein, it should be appreciated that any arrangement calculated toachieve the same purpose may be substituted for the specific embodimentsshown. This disclosure is intended to cover any and all adaptations orvariations of various embodiments. It is to be understood that the abovedescription has been made in an illustrative fashion, and not arestrictive one. Combinations of the above embodiments, and otherembodiments not specifically described herein will be apparent to thoseof skill in the art upon reviewing the above description. Thus, thescope of various embodiments includes any other applications in whichthe above compositions, structures, and methods are used.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

The invention claimed is:
 1. An apparatus, comprising: at least onememory; and logic coupled to the at least one memory, the logic to:receive chromatography data originating from a chromatography system;process the chromatography data using a computational machine learningmodel to diagnose a failure of the chromatography system by identifyingone of a change in peak retention time, a change in peak area, a changein peak shape, a change in peak width, a change in system pressure, or achange in a number of peaks relative to respective benchmark values,wherein the computational learning model has been trained on trainingchromatography data to identify such a failure; and output a diagnosisof the failure.
 2. The apparatus of claim 1, the chromatography datacomprising a chromatogram.
 3. The apparatus of claim 1, the logicfurther configured to determine at least one cause of the failure. 4.The apparatus of claim 1, the computational machine learning modelcomprising at least one of a deep learning (DL) computational model, aneural network, a convolutional neural network (CNN), a recurrent neuralnetwork (RNN), or a multilayer perceptron (MLP) model.
 5. The apparatusof claim 1, the logic to present the diagnosis on a display deviceoperably coupled to the logic.
 6. A method performed by a processor ofan apparatus, comprising: receiving chromatography data originating froma chromatography system at the processor; processing the chromatographydata with the processor using a computational machine learning model todiagnose a failure of the chromatography system by identifying one of achange in peak retention time, a change in peak area, a change in peakshape, a change in peak width, a change in system pressure, or a changein a number of peaks relative to respective benchmark values, whereinthe computational learning model has been trained on trainingchromatography data to identify such a failure; and outputting adiagnosis of the failure on a display device.
 7. The method of claim 6,wherein the chromatographic data comprises a chromatogram.
 8. The methodof claim 6, further comprising, with the processor, determining thecause of the failure.
 9. The method of claim 6, wherein thecomputational machine learning model comprises at least one of a deeplearning (DL) computational model, a neural network, a convolutionalneural network (CNN), a recurrent neural network (RNN), or a multilayerperceptron (MLP) model.
 10. A non-transitory computer-readable storagemedium storing processor-executable instructions that when executed bythe processor cause the processor to: receive chromatography dataoriginating from a chromatography system at the processor; process thechromatography data with the processor using a computational machinelearning model to diagnose a failure of the chromatography system byidentifying one of a change in peak retention time, a change in peakarea, a change in peak shape, a change in peak width, a change in systempressure, or a change in a number of peaks relative to respectivebenchmark values, wherein the computational learning model has beentrained on training chromatography data to identify such a failure; andoutput a diagnosis of the failure on a display device.
 11. Thenon-transitory computer-readable storage medium of claim 10, wherein thechromatographic data comprises a chromatogram.
 12. The non-transitorycomputer-readable storage medium of claim 10, further storinginstructions that when executed by the processor cause the processor todetermine the cause of the failure.
 13. The non-transitorycomputer-readable storage medium of claim 10, wherein the computationalmachine learning model comprises at least one of a deep learning (DL)computational model, a neural network, a convolutional neural network(CNN), a recurrent neural network (RNN), or a multilayer perceptron(MLP) model.